We propose a new probabilistic model for general-purpose medical image registration that builds upon the mutual information registration criterion. It centers around a spatial interpolation technique that assumes latent voxel-wise correspondences between the images being registered. By exploiting these latent variables, we derive dedicated optimization and MCMC sampling techniques that only involve closed-form iterative updates. When applied to nonlinear registration, an efficient demons-like optimization algorithm is obtained that shows robust out-of-the-box performance across a variety of monomodal and multimodal registration tasks. We also demonstrate a corresponding sampler that can quantify, for the first time, uncertainty in multimodal registration scenarios with very high-dimensional 3D deformations. Our code, which we call BINDER (Bayesian INference for DEformable Registration), is freely available at https://github.com/ste93ste/BINDER.
Findings highlight that registration accuracy depends strongly on matching the induced deformation prior to the target motion pattern, and support prior-deformation matching as a practical design principle for medical image registration.
Hengjie Liu, Chushu Shen, Dan Ruan et al.· 0 citations
Abstract Medical image registration, encompassing both rigid and non-rigid transformations, plays a fundamental role in medical image analysis. Applications such as therapy planning, longitudinal disease analyses, and model-based segmentation benefit from the use of registration. While rigid registration suffices for a...
Elina Gastreich de Llanes, Melina Wördehoff, Lukas Förner et al.· Current Directions in Biomed...· 0 citations
Recursive Uncertainty-Gated Image Registration (RUGI), an algorithm for iteratively refining deformation fields predicted by learning-based registration models, demonstrates that spatially selective iterative refinement provides an effective strategy to improve registration accuracy at inference-time.
Clara Rodrigo Gonzalez, Oscar Bates, F. Ng et al.· 0 citations
This work proposes a framework to directly learn the continuous-time solution of a non-autonomous ODE formulated as a two-parameterflow map by enforcing cocycle consistency, a fundamental structural property of time-varying flows, by enforcing cocycle consistency during training.
This work introduces a latent-to-latent flow technique for stochastic segmentation of medical volumes via encoded representations of both the image and label space, improving efficiency up to 14x compared with full resolution models while maintaining clinically relevant performance.
Omar Todd, Sooha Kim, Raghav Mehta et al.· 0 citations
This study introduces a novel 3D registration framework centered on a dynamic wavelet transform module that achieves superior registration fidelity, highlighting its potential for practical clinical implementation.
Bo-Hua Chu, Bao-Ju Zhang, Bo Zhang et al.· Interdisciplinary Sciences C...· 0 citations
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